High Resolution Filter-Bank-Based Power Spectrum Estimation

This example shows how to perform high resolution spectral analysis by using an efficient polyphase filter bank sometimes referred to as a channelizer.

Open the Model

Exploring the Example

This example compares full band and subband spectral estimators. Both spectral estimators use polyphase filter bank (channelizer) implementations which provide good resolution and improved accuracy when compared to Welch-method-based estimators. See High Resolution Spectral Analysis in MATLAB for a comparison between filter bank and Welch-based spectral estimators.

In this example, the full band estimator requires a 512-phase polyphase FIR filter and a 512-point FFT in order to compute the spectral estimate. The sinusoid frequencies in each subband are spaced further apart as the frequency increases. The idea is to setup a case in which higher frequency resolution is required at the low frequency band and lower resolution is required at higher frequency bands.

The sub-band approach is more efficient. It uses an 8-phase polyphase FIR filter and an 8-point FFT to divide the broadband signal into 8 sub-bands. Subsequently, a 64 band filter bank estimator (itself containing a 64-phase polyphase FIR filter and a 64-point FFT) is used with the low frequency sub-band in order to compute the spectral estimate with the same resolution as the full band estimator. The same implementation is used for the mid-low frequency band.

Simulate the Model

For the mid-high frequency band, the sinusoids are spaced further apart. Hence, a 32 band filter bank estimator is used. For the high-frequency band, we use a 16 band filter bank estimator.

References

harris, f. j. Multirate Signal Processing for Communications Systems, Prentice Hall PTR, 2004.